An Event Detection Mechanism with Deep Feature Extraction and Optimal Loss Function Based XGBoost Classifier

B  Manjula; P.  Venkateshwarlu

doi:10.18502/fbt.v11i3.15879

B Manjula Department of Computer Science, University College, Kakatiya University, Telangana, India
P. Venkateshwarlu Department of Computer Science, Vaageswari College of Engineering, Telangana, India

DOI: https://doi.org/10.18502/fbt.v11i3.15879

Keywords: Support Vector Machine; Random Forest Classifier; Deep Stacked Auto-Encoder; XGBoost Classifier Extreme Gradient Boost Classifier; Classification; Event Detection.

Abstract

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations.

Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders.

Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders.

Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well.

Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.